Present work: In this work, we adopt a pragmatic approach that allows us to utilize the existing infrastructure developed at Pinterest to train heterogeneous graph models. The existing infrastructure includes the random-walker system, Pixie [1]}, that loads the graph (without node features) in a single AWS x1.32xlarge (2 TB RAM, 128 cpus) instance and utilizes C++ routines to identify the local graph neighborhood of the nodes. We also utilize the existing model training and feature extraction workflows. The approach can be summarized as follows. Given a heterogeneous graph we first decompose it into multiple bipartite graphs. We then compute the local graph neighborhood of pins with the help of Pixie. To efficiently aggregate the node features and node neighbor features from multiple bipartite graphs, we propose a transformer based MultiBiSage model. Given \(k\)  bipartite graphs, MultiBiSage learns \(k\)  pin embeddings with the help of transformer [2]} model where each pin embedding corresponds to each input bipartite graphs. These pin embeddings are then aggregated with another transformer layer that generates the final pin embedding.
The contributions of our work are summarized as follows
